Prostate Tumor Blueprint Identifies Most Effective Drugs

A team of researchers applied a comprehensive set of analytical tools to deadly cases of metastatic prostate cancer to produce a detailed map of the complex networks of interactions that enable cancer cells to proliferate and evade treatment.

Through the collaborative effort between UC Santa Cruz and UCLA, the researchers were also able to develop a computational approach for analyzing patient-specific data in order to help physicians choose the most effective drugs for each individual patient. In a study published in Cell, researchers began with clinical tissue samples obtained at autopsy from patients with metastatic prostate cancer.

They conducted a range of sophisticated analyses to characterize the cancer cells from each patient in unprecedented detail. Conducting a novel computational analysis of the resulting dataset produced personalized diagrams of signaling pathways in the cancer cells of each patient, the details of which suggest potential targets for therapy.

“It’s like having a blueprint for each tumor,” said senior corresponding study author Josh Stuart. “This is our dream for personalized cancer therapy, so we’re not just guessing any more about which drugs will work but can choose drug targets based on what’s driving that patient’s cancer.”

Although cancer genomics promises to enable personalized cancer treatment by identifying the genetic mutations that drive a patient’s tumor cells, interpreting the genomic data is a challenge. This is because the effects of mutations and other genetic changes in cancer cells play out in the complex network of molecular interactions or signaling pathways involved in cell growth, proliferation, and other areas of cancer biology, according to the study.

However, when researchers mapped the key pathways in the current study active in prostate cancer cells, they were able to identify the “master switches” in pathways that could potentially be targeted with drugs.

“Therapies for metastatic prostate cancer are urgently needed,” said senior study author Dr. Owen Witte. “This type of interdisciplinary research is critical as we seek to pinpoint the cellular changes occurring in aggressive prostate cancer and cross new boundaries in understanding the disease.”

Phosphorylation is the key in many signaling pathways. A major component of the study was a comprehensive analysis of the phosphoproteome of prostate cancer cells and tumors, which revealed the changes in the phosphorylation state of cellular proteins.

The phosphoproteomics work was led by co-first study author, Justin Drake, and produced a new encyclopedia of protein phosphorylation in prostate cancer cells and tissues. The computational analyses were led by co-first study author, Evan Paull, and involved the integration of the phosphoproteomic data with genomic and gene expression datasets in order to give a unified view of the activated signaling pathways in late stage prostate cancer.

“Having the phosphoproteomics data in addition to the traditional genomics and transcriptomics enabled us to get a more comprehensive view of aberrant signaling in this disease,” Paull said. “We developed a method to integrate these multiple large datasets to understand what’s driving the disease in individual patients.”

The main form of treatment used to treat advanced prostate cancer is androgen deprivation. The anti-androgen therapies target the androgen synthesis or the androgen receptor, but most metastatic prostate cancer cases end up becoming resistant to these therapies.

Some of the mechanisms behind this resistance to anti-androgen therapies were revealed in the current study. Researchers stated that in many cases a mutation resulted in changes to the androgen receptor protein.

However, other cases revealed alternative kinase signaling pathways that allowed the cancer cells to continue to grow, despite the androgen-receptor signaling being blocked. When researchers examined the individual profiles based on the analysis of each of the patient’s tumor cells, it revealed clinically relevant information that could be used to prioritize the drugs that would most likely to be effective and beneficial in these cases.

The tools used to generate the individual profiles is called pCHIPS, and researchers created an online pCHIPS resource that allows users to make patient-specific network predictions based on their own data, and also visualize the results using the pCHIPS methodology. When the methods were applied to prostate cancer cell lines, researchers found that by using either genomics data or phosphoproteomics data alone, it could accurately predict drug sensitivity.

These findings are important because the comprehensive set of analyses conducted on the study’s clinical samples are unlikely to be available to most patients. Authors noted, however, that the clinical use of genomics is continuing to grow.

By using the integrated datasets from multiple analyses, it allowed researchers to develop a generic model of the signaling networks that are involved in metastatic prostate cancer. The pCHIPS tool uses the generic model and refines it based on patient-specific data, such as the genetic mutations in a patient’s cancer cells, explained Stuart.

“For now it’s a research tool, but the hope is to have a strategy like this to use in the clinic,” Stuart said. “These mutations in the genome create a lot of havoc in the cell, and trying to interpret the genomic information can be overwhelming. You need the computer to help you make sense of it and find the Achilles heel in the network that you can hit with a drug.”